Bayesian Spatio-Dynamic Modeling in Cell Motility Studies: Learning Nonlinear Taxic Fields Guiding the Immune Response

DOI10.1080/01621459.2012.655995zbMath1443.62390OpenAlexW2045316537WikidataQ58045299 ScholiaQ58045299MaRDI QIDQ4648522

Michael D Cahalan, Mike West, Ioanna Manolopoulou, Melanie P. Matheu, Thomas B. Kepler

Publication date: 9 November 2012

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/01621459.2012.655995

zbMATH Keywords

Markov chain Monte Carlochemotaxisstate-space modelsnonlinear stochastic dynamicsBayesian kernel regressiohierarchical dynamic modelsimmune response monitoringpotential field gradientsradial basis regressionsingle-cell trackingtaxic responses

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (1)

Statistical inference for dynamical systems: a review

Cites Work

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• Exponential convergence of Langevin distributions and their discrete approximations
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• DATA AUGMENTATION AND DYNAMIC LINEAR MODELS

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